Learn how to use external libraries and languages on resident, relational data with our quickstarts, tutorials, and how-to articles.

R and Python libraries in [SQL Server machine learning](what-is-sql-server-machine-learning.md) include base distributions, machine learning algorithms, and functions for conducting high-performance analytics at scale, without having to transfer data across the network.

Java code execution uses the same extensibility framework as R and Python, but does not include data science and machine learning function libraries.

|  | Open-source R, extended with [RevoScaleR](https://docs.microsoft.com/machine-learning-server/r-reference/revoscaler/revoscaler) and Microsoft AI algorithms in [MicrosoftML](https://docs.microsoft.com/machine-learning-server/r-reference/microsoftml/microsoftml-package). These libraries give you forecasting and prediction models, statistical analysis, visualization, and data manipulation at scale. <br/>R integration starts in [SQL Server 2016](./install/sql-r-services-windows-install.md) and is also in [SQL Server 2017](./install/sql-machine-learning-services-windows-install.md). |

|  | Python developers can use Microsoft [revoscalepy](https://docs.microsoft.com/machine-learning-server/python-reference/revoscalepy/revoscalepy-package) and [microsoftml](https://docs.microsoft.com/machine-learning-server/python-reference/microsoftml/microsoftml-package) libraries for predictive analytics and machine learning at scale. Anaconda and Python 3.5-compatible libraries are the baseline distribution. <br/>Python integration starts in [SQL Server 2017](./install/sql-machine-learning-services-windows-install.md). |

|  | Java developers can use the [Java language extension](java/extension-java.md) to wrap code in stored procedures or in a binary format accessible through Transact-SQL. <br/>Java integration is currently [SQL Server 2019 only](./install/sql-machine-learning-services-ver15.md). |